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Effective and timely diagnosis and treatment of ocular diseases is essential for swift recovery of the patients. Among ocular diseases, cataract and glaucoma are the most prevalent globally and need adequate attention. The present paper aims to develop an optimised deep learning based convolutional neural network (CNN) for the multi-classification of ocular diseases (normal, glaucoma and cataract). Three pre-trained CNNs (SqueezeNet, Darknet-53, EfficientNet-b0) were optimised concerning batch size (6/8/10) & optimiser type (SGDM, RMSProp, Adam) for obtaining maximum possible accuracy in the detection of multiple ocular diseases (cataract & glaucoma). Darknet-53 (batch size-6, optimiser type-Adam) gave the highest accuracy of 99.4% for a test sample of 1000 images. The performance metrics of Darknet-53 have been computed using a confusion matrix. Confusion matrix is also applied to calculate accuracy, sensitivity, specificity, f1 score and receiver operating curve (ROC). Through comparative performance analysis of the three CNNs, SqueezeNet, Darknet-53 and EfficientNet-b0 achieved the highest accuracy of 95%, 99.4% and 90%, respectively. The results indicate the importance of batch size and optimiser type on the performance of CNN models. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most prevalent malignancies and the primary origin of cancer-related deaths among females worldwide. Ultrasound image segmentation plays a crucial role in identifying breast tumours by precisely delineating the boundaries of the tumour within the images. Deep learning segmentation networks such as DeepLabV3+ have been used in the literature for this purpose. The coupling of DeepLabV3+ with base convolutional neural networks (CNN) can play a key role in its accuracy in tumour detection. The present study investigates the segmentation performance of four combinations of DeepLabV3+ segmentation network by coupling with the four base decoder CNN networks: DarkNet53, SqueezeNet, EfficientNet-b0 and DarkNet19. The accuracy of segmentation is confirmed using standard segmentation error metrics such as global and mean accuracy, mean and weighted Intersection over union (IoU), Boundary F1 (BF) Score and Dice Score. DeepLabV3+ coupled with DarkNet53 and EfficientNet-b0 as the decoder CNNs performed better than the other two combinations with a global accuracy of 96.50% and 96.18%. Precise tumour delineation can assist in tumour growth monitoring and treatment planning by providing detailed information on tumour size, shape, and location; thereby aiding in patient management and follow-up care. [ABSTRACT FROM AUTHOR]

It is imperative to understand the significance of the early diagnosis of pneumonia using a convolutional neural network (CNN) to reduce the processing time and increase the quality of treatment that is delivered to the patient. We have implemented transfer learning for processing of available datasets and constructed an ensemble of 3-CNNs: SqueezeNet, ResNet-50 and EfficientNet-b0. In this work, a multiclassification model has been built that can help in early pneumonia diagnosis. The chest X-rays of patients have been classified in 2-stages. In the first stage, the EfficientNet-b0 convolutional neural network with 99% accuracy is employed to diagnose whether the patient's chest X-ray is Normal/Abnormal. If the output of the first stage is found abnormal then the chest X-ray is processed to the second stage wherein ResNet-50 with 97% accuracy is employed to diagnose the pneumonia type, pneumonia bacteria/pneumonia virus. Further, performance metrics have been computed from the confusion matrix for both stages of X-ray. Also, it is imperative to mention that a pneumonia diagnosis app has been developed in Matlab-2023 for the ease of patients who can self-evaluate the scan report and understand the course of clinical treatment. [ABSTRACT FROM AUTHOR]

In this work, the segmented dental X-ray images obtained by dentists have been classified into ideal/minimally compromised edentulous area (no clinical treatment needed immediately), partially/moderately compromised edentulous area (require bridges or cast partial denture) and substantially compromised edentulous area (require complete denture prosthesis). A total of 116 image dental X-ray dataset is used, of which 70% of the image dataset is used for training the convolutional neural network (CNN) while 30% is used sfor testing and validation. Three pretrained deep neural networks (DNNs; SqueezeNet, ResNet-50 and EfficientNet-b0) have been implemented using Deep Network Designer module of Matlab 2022. Each of these CNNs were trained, tested and optimised for the best possible accuracy and validation of dental images, which require an appropriate clinical treatment. The highest classification accuracy of 98% was obtained for EfficientNet-b0. This novel research enables the implementation of DNN parameters for automated identification and labelling of edentulous area, which would require clinical treatment. Also, the performance metrics, accuracy, recall, precision and F1 score have been calculated for the best DNN using confusion matrix. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot's intention to serve based on the human's location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation. [ABSTRACT FROM AUTHOR]

This paper presents an innovative Hybrid Electric Vehicle (HEV) configuration utilizing a fuel cell as the primary energy source and an onboard Photovoltaic (PV) array as a supplementary source. The system features an advanced Model Predictive Control (MPC) enhanced by a Recurrent Neural Network (RNN) to manage the induction motor efficiently. Key components include a PV array, a fuel cell, and an electrolyzer. The PV array supplements the fuel cell during optimal sunlight conditions, while excess energy during idle periods is converted to hydrogen via the electrolyzer and stored in a hydrogen tank for future use. A quadratic bidirectional buck-boost converter (QBBC) regulates voltage, ensuring compatibility between energy sources and the motor. The system's performance is evaluated under various sunlight and speed conditions, with the RNN-based MPC compared to an Artificial Neural Network-based MPC (ANN-MPC) and a traditional Proportional-Integral (PI) controller. An incremental conductance algorithm is implemented for Maximum Power Point Tracking (MPPT) to optimize PV power extraction. The RNN model predicts motor speed, enhancing control precision. Simulations in MATLAB/SIMULINK reveal that the RNN-based MPC outperforms ANN-MPC and PI controllers, demonstrating improved efficiency and speed control. This work contributes to advancing intelligent and energy-efficient HEV technologies. [ABSTRACT FROM AUTHOR]

Tapping into digital footprints on social media, this research focuses on providing new insights into detecting depression through textual analysis. Initially, emotional raw data found in social media posts, aimed particularly at the expressions of anger, fear, joy, and sadness, were collected and analyzed. These emotions, each scored by their intensity, offer a quantifiable view into the users' mental state, serving as possible depression markers. Central to the methodological framework adopted is the binary classification system, which classifies texts into depressive or non-depressive states, well founded by the patterns unearthed from the data. The proposed model rigorously trains Artificial Intelligence/Machine Learing (AI/ML) models to traverse through the complexities of natural language, concentrating on noticing delicate indications that signal depression. The introduced models are tested and measured with accuracy, precision, recall, and F1-score. RoBERTa, DistilBERT, and Electra are the transformer-based models emphasized in this research. Their performance is critically evaluated, with the results denoting particular capabilities in understanding and contextualizing language, which is the key advantage in the early identification of mental health issues. This research stands at the intersection of technology and mental health, revolutionizing mental health monitoring and intervention. [ABSTRACT FROM AUTHOR]

In this study, we performed milling machining on carbon-epoxy polymer composites and jute-epoxy composites using a CNC vertical machining center. We focused on spindle speed, feed rate, depth of cut, and flute number and analyzed the cutting force and surface roughness. The optimal parameter combination to reduce cutting force in both composites was as follows: S = 600 rpm, FR = 100 mm/min, DOC = 0.25 mm, and FN = 6. The jute-epoxy composites required less cutting force (11.85 N/m2) compared to the carbon-epoxy composites (18.77 N/m2). The average surface roughness of the carbon-epoxy composites (6.685 µm) is higher than that of the jute-epoxy composites (3.08 µm). The type of reinforced material used greatly affects the cutting force and surface roughness during milling. [ABSTRACT FROM AUTHOR]

Breast cancer analysis is critical for clinical diagnosis and treatment because of its association with abnormal cell growth and tumor formation, which pose a significant health concern among women. Researchers have extensively explored traditional and individual deep learning algorithms, such as convolutional neural networks (CNNs) and artificial neural networks (ANNs), for this purpose. However, reliance on mono-based models often results in suboptimal classification accuracy in medical diagnosis. To get around this problem, we suggest a new method that combines generative adversarial networks (GANs) and residual neural networks (ResNets) in a way that works well together to accurately find breast cancer in histopathological images. Our approach leverages an enriched dataset to ensure better generalization during training. We employ GANs to augment the training dataset by generating synthetic images, which enhances feature recognition and improves the robustness of the model. We then use this augmented data to fine-tune ResNet, a robust deep learning architecture, for classification tasks. The GAN-ResNet framework uses discriminatory features taken from the generated images by the GAN discriminator. This combines the power of GANs for discrimination with the power of ResNet for classification. We specifically fine-tuned the final model layer for binary classification, enabling accurate differentiation between malignant and benign breast tissue. Additionally, we adapted the loss function to address imbalances in the medical dataset, ensuring a more robust and accurate model. Our proposed model demonstrates a remarkable 98% accuracy in analyzing histopathological images, validating its efficacy for early breast cancer detection. This innovative approach underscores its potential for significant advancements in clinical diagnostics. This version highlights the novelty of combining GANs with ResNet and emphasizes the unique aspects of your approach, such as the use of synthetic images for data augmentation and the adaptation of the loss function to handle dataset imbalances. [ABSTRACT FROM AUTHOR]

Plasma-surface modification method (PSMM) is an efficient and inexpensive surface processing method for various materials and has generated great interest in the field of biomedical engineering. This paper focuses on the numerous conventional plasma methods and experimental approaches applied to materials research for suitable biomedical applications, including plasma deposition, laser plasma deposition, plasma sputtering and etching, plasma polymerization, plasma spraying, plasma implantation, and so on. The distinctive benefit of plasma modification is its biocompatibility and surface properties can be enhanced on a selective basis while the bulk characteristics of the materials stay unaltered. Existing materials can hence be used and the requirement for new materials may be circumvented thereby reducing the time for the development of novel and efficient biomedical devices. [ABSTRACT FROM AUTHOR]

The long-term viability of reimplanted teeth is closely linked to the preservation of periodontal ligament (PDL) cells. Dental tissues possess a unique regenerative capacity, setting them apart from most other bodily tissues. Understanding the intricate processes governing repair and regeneration in oral tissues has proven to be a considerable challenge. Numerous authors have introduced various storage media designed to support the viability of PDL cells. This paper aims to present a comprehensive overview of the diverse range of storage media currently at our disposal. [ABSTRACT FROM AUTHOR]

Mining frequent patterns from voluminous datasets termed under 'Big data' and having inherent uncertainties poses a significant challenge. Minor changes carried out on the databases like; addition, deletion or modification of items should not lead to scanning the whole database. Besides, a number of algorithms proposed to handle these issues are effective, but their basis of mathematics and way of installation are complex. Keeping the above points in mind, we have proposed an approach, which innovatively combines the models Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM) serially to improve the prediction accuracy. Here, the LightGBM brings its tree-based learning algorithms optimized for speed and performance, while LSTM contributes its advanced sequence modeling capabilities, effectively resolving the vanishing gradient dilemma that often plagues recurrent networks. Our approach is applied to the healthcare sector in general and particularly in the early detection of Breast Cancer from a dataset obtained from Kaggle, yielding outstanding results as are evident from the scores; precision rates of 0.92 for predicted negatives and 0.93 for predicted positives, recall rates of 0.96 for negatives and 0.88 for positives, alongside F1-scores of 0.94 and 0.90, respectively. With a comprehensive accuracy of 0.93 across 188 samples, our model demonstrates a remarkable potential for early medical diagnosis, outperforming existing single-model solutions. The robustness of our approach is further validated by the consistency of performance across various metrics, highlighting its suitability for deployment in high-stakes domains where predictive accuracy is paramount. [ABSTRACT FROM AUTHOR]

The trending and spotlight research in the field of mobile robots in an indoor environment is the parking of autonomous multi-mobile robots. The proposed research work presents a co-operative, unified autonomous multi-robot system (MRS) for parking in the indoor environment. In the research work, initially MRS establishes by themselves a flock using the behavioral control mechanism and is capable of swapping leadership behavior based on event-driven conditions. The Real time challenges of MRS flocking in an indoor environment are parking and obstacle avoidance with rendezvous approach. In this regard, the proposed hardware-based accelerators for MRS flock have been developed using Verilog HDL and deployed on Field Programmable Gate Array (FPGA) to perform obstacle avoidance with rendezvous and parking at versatile modes. In the process of parking by MRS, the leader evaluates parking slot dimensions and drives the flock towards the parking. Due to versatile parking modes, the MRS can park in parallel/perpendicular and angular parking. The proposed approach has been implemented and validated with experiments using Vivado tools and Xilinx FPGA Zynq-7000 SoC ZC702. [ABSTRACT FROM AUTHOR]

3D printing technology is rapidly transforming paediatric dentistry by enabling personalized treatment approaches, reducing patient anxiety, and improving treatment outcomes. The versatility of 3D printing applications in this field continues to expand, promising a brighter future for young dental patients and practitioners alike. This abstract underscores the importance of staying abeam of these advancements and embracing the potential of 3D printing technology in paediatric dentistry. KEYWORDS: 3D printing, Additive manufacturing, Direct metal laser sintering, Bioprinting, Pediatric dentistry. [ABSTRACT FROM AUTHOR]

Background: The present study was carried out to compare macro-microscopy, powder microscopy and HPTLC analysis of six species such as Senna auriculata (L.) Roxb., Senna alata (L.) Roxb., Senna alexandrina Mill., Senna occidentalis (L.) Link., S. uniflora (Mill.) Irwin and Barneby, S. tora (L.) Roxb. of the genus Senna Mill. belongs to the family Caesalpiniaceae. Materials and Methods: Six selected Senna species were collected, shade dried and pharmacognostic study were performed used techniques such as microscopy and HPTLC analysis. Results: Seed macro-morphology of the selected six species shows significant variations in size, shape, colour, ornamentation and number of seeds per pod. The basic cellular structure of six Senna species was anatomically similar but variation was observed in number of layers. Microscopic characters of the selected species revealed key diagnostic features that can help to determine the relationship between the closely related species; macrosclerieds and osteosclerdies were observed in Senna auriculata and S. occidentalis. Powder microscopy of selected six Senna species varied in colour, texture and odour. Numerous rosette crystals in cotyledonary cells were found only in Senna tora which is a significant variation from other five Senna species. HPTLC finger print profiling was performed with ethanol extract at 254 nm, 366 nm and 575 nm derivatization using vannilin-sulphuric acid and the results were documented. In 254 nm 14 bands were separated but the major peak at Rf 0.31 (22.24%) appeared in S. alexandrina. Conclusion: A detailed morphological and pharmacognostical evaluations play an important role to avoid misidentification and delimitation of selected six Senna species and HPTLC profiling is an additional analytical tool for species identification. [ABSTRACT FROM AUTHOR]

Introduction: Diabetes is a chronic disease with life altering consequences. It not only forces one to question and alter one's life style but also thrusts the added responsibility of self-care upon them. Patients are required to maintain controlled levels of glycalated hemoglobin (HbA1c). But up to 50 percent fail to do so and land up with a wide array of complications. One among these many complications is the psychiatric comorbidity of depression. Materials and Methods: The present study is a cross sectional study conducted at Department of Psychiatry, Shimoga Institute of Medical Sciences, Shimoga, Karnataka from the period of February 2022 to October 2022. The study population was determined to be 120 in number. Participants who were diagnosed with diabetes mellitus under American Diabetic association criteria were randomly selected from outpatient department of General medicine and referred to Department of Psychiatry, Shimoga Institute of Medical Sciences, Shimoga. Results: 75% of the study population had history of diabetes mellitus for more than 5 years of duration. More than half of the study population did not have any substance use (56.7%), however use of nicotine (13%) or alcohol (11%) or both (2%) were found in 43.3% of the population in total. Systemic hypertension was found to be the most common medical co-morbidity accounting to 88.3% of the study participants. More than 60% of the study population did not have any family history of depression. 31.7% of the study population expressed worthlessness (21.7%), death wishes and suicidal thoughts (8.3%) or attempts (1.7%). Conclusion: Early screening and appropriate intervention may lead to improvement in both mental and physical wellness along with prevention of suicides in these patients. Prevalence of depressive disorders among diabetes mellitus patients is increasing in number and severity. We would like to stress upon the need for screening depression among all patients who have been diagnosed with diabetes mellitus to ensure early detection, diagnosis, management, and suicide prevention. [ABSTRACT FROM AUTHOR]

Autonomous grounded vehicle-based social assistance/service robot parking in an indoor environment is an exciting challenge in urban cities. There are few efficient methods for parking multi-robot/agent teams in an unknown indoor environment. The primary objective of autonomous multi-robot/agent teams is to establish synchronization between them and to stay in behavioral control when static and when in motion. In this regard, the proposed hardware-efficient algorithm addresses the parking of a trailer (follower) robot in indoor environments by a truck (leader) robot with a rendezvous approach. In the process of parking, initial rendezvous behavioral control between the truck and trailer robots is established. Next, the parking space in the environment is estimated by the truck robot, and the trailer robot parks under the supervision of the truck robot. The proposed behavioral control mechanisms were executed between heterogenous-type computational-based robots. Optimized sensors were used for traversing and the execution of the parking methods. The truck robot leads, and the trailer robot mimics the actions in the execution of path planning and parking. The truck robot was integrated with FPGA (Xilinx Zynq XC7Z020-CLG484-1), and the trailer was integrated with Arduino UNO computing devices; this heterogenous modeling is adequate in the execution of trailer parking by a truck. The hardware schemes were developed using Verilog HDL for the FPGA (truck)-based robot and Python for the Arduino (trailer)-based robot. [ABSTRACT FROM AUTHOR]

Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs. This ILDA approach consists of four major steps such as data dimension reduction, distance similarity index introduction, double cluster head technique and node dormancy approach. This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head (CH) selection technique. According to node's position and residual energy, optimal cluster-head function is generated, and every CH is elected by this formulation. For a 3D spherical structure, under the same network condition, the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering (IDHC) is compared with Distributed Energy-Efficient Clustering (DEEC), Hybrid Energy Efficient Distributed (HEED) and Stable Election Protocol (SEP) techniques. It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput, network residual energy, network lifetime and first node death round. [ABSTRACT FROM AUTHOR]

Traditionally, the leaves of Clematis wightiana have been used in the treatment of rheumatism, indigestion, headaches, varicose veins, bone problems, nasal congestion and sinus. The present study was conducted to evaluate the phytochemical profile, quantification of secondary metabolites and free radical scavenging capacity of C. wightiana leaf. The total phenolic, tannin and flavanoid content of C. wightiana leaves were quantified and were found to be higher in the ethyl acetate extract. Subsequently, the extracts were subjected to appraise their antioxidant capacity by availing various in vitro antioxidant assays namely DPPH radical scavenging assay, ABTS assay, Phosphomolybedenum assay, Ferric Reducing assay, Superoxide Radical Scavenging assay and Reducing power assay. The results of the antioxidant assays revealed that the ethyl acetate extract of C. wightiana leaves possess better free radical scavenging activity than other solvent extracts. Thus, the finding of the study elucidates the perception on phytochemical and bioactivity of C. wightiana which could be used in development of phytotherapeutics to enchance human health. [ABSTRACT FROM AUTHOR]

In the present work, vibration control of a simply supported plate with line type and cross type piezoelectric (PZT) patches are investigated with and without actuation voltage. The plate is modeled under the assumption of Kirchhoff's Plate theory. The mass of PZT patches remain constant in all cases. In case of actuation, applied voltage considered are 1, 2 and 3 mV. The external excitation to the plate is in the form of harmonically varying point load of 1 mN. It is noticed that cross type PZT patch is more effective in deflection suppression of plate than that of line type PZT patch at 3 mV of actuation at patch thickness of 0.75 μm. Suppression of central deflection of plate for line type and cross type PZT patches are obtained in different frequency bands of (175–185 Hz) and (870–880 Hz) respectively. [ABSTRACT FROM AUTHOR]

Background: Central venous catheterization is a vital intervention in critically ill patients. The purpose of this study was to compare the procedural parameters and complications associated with anterior and posterior approaches of IJV catheterization under real time ultrasound guidance in critically ill patients. Material And Methods: In this prospective randomised study, 90 patients admitted in various ICUs were randomly allocated two groups of 45 each, including both males and females aged between 18 to 80 years of age requiring central venous catheterization for various indications. Result: The first attempt success rates were comparable between both the groups. The venous visualization time was 38.52 seconds in Group A and 14.65 seconds in Group P (p<0.001). The venous puncture time and the duration of catheterization was found to be 47.60 sec & 2 minutes in Group A respectively and 24.16 sec & 1 minute 32 sec in Group P respectively (p<0.001). No statistically significant differences were found between the two groups in terms of incidence of carotid arterial puncture, haematoma formation and catheter displacement. Conclusion: It was observed that the posterior approach is better than anterior approach of USG guided IJV catheterization as it improves the accuracy, reduces the access time and duration of catheterization & leads to fewer incidences of immediate complications like carotid arterial puncture & subsequent haematoma formation. [ABSTRACT FROM AUTHOR]

Author summary: We report a case of rabies encephalitis in a 4½-year-old male child with an exposure to a suspect rabid dog. The child developed rabies 25 days after receiving postexposure prophylaxis. Rabies immunoglobulin (RIG) is currently administered according to body weight. In high-risk exposures over the head and neck, local administration of RIG over and above the body weight depending on the site, size, and severity of exposure may help to prevent rabies death. There is a need for further studies to generate new evidence in this regard. [ABSTRACT FROM AUTHOR]

In this study, an atmospheric pressure dielectric barrier discharge-based argon plasma jet has been modeled using COMSOL Multiphysics, which is based on the finite element method. The fluid dynamics and plasma modules of COMSOL Multiphysics code have been used for the modeling of the plasma jet. The plasma parameters, such as electron density, electron temperature, and electrical potential, have been examined by varying the electrical parameters, that is, supply voltage and supply frequency for both cases of static and with the flow of argon gas. The argon gas flow rate was fixed at 1 l/min. Ring electrode arrangement is subjected to a range of supply frequencies (10–25 kHz) and supply voltages (3.5–6 kV). The experimental results of the ring electrode configuration have been compared with the simulation analysis results. These results help in establishing an optimized operating range of the dielectric barrier discharge-based cold plasma jet in the glow discharge regime without arcing phenomenon. For the applied voltage and supply frequency parameters examined in this work, the discharge was found to be consistently homogeneous and displayed the characteristics of atmospheric pressure glow discharge. [ABSTRACT FROM AUTHOR]

In the present study, qualitative phytochemical screening and evaluation of antioxidant activity of various solvent extracts of Cayratia pedata fruit were analyzed. The powdered fruit samples were subjected for the extraction by using different solvents such as petroleum ether, chloroform, ethyl acetate, methanol and hot water. The extracts were subjected to quantification of total phenolics, tannin and flavonoids. The in vitro antioxidant assay such as DPPH, ABTS, FRAP, superoxide radical scavenging assay, phosphomolybdenum, Reducing power assay and GC-MS analysis were done. Among the different extracts of C. pedata fruit were studied, the ethyl acetate extract of C. pedata fruit depicted the maximum amount of phenolics (312.82 mg GAE/g extract), tannin (298.01 mg GAE/g extract) and flavonoids (222.03 mg RE/g extract). Highest percentage of DPPH (IC50: 14.47 µg/mL), ABTS (56631.94 µM TE/g extract), FRAP (822.6 mM Fe (II)/mg extract), Superoxide (68.72%) and phosphomolybdenum radical scavenging activity (86.6 mg AAE/g extract) were recorded in the ethyl acetate extract of C. pedata fruit. GC-MS analysis confirmed the presence of different phytochemicals in each fruit extracts of C. pedata of which 10 major phytoconstituents were identified. The present study provides evidence that ethyl acetate extract of C. pedata fruit possesses excellent in vitro antioxidant activity, which can be a potential source of natural antioxidants, should be further exploited for its use in clinical medicine. [ABSTRACT FROM AUTHOR]

In the present study, qualitative phytochemical screening and evaluation of antioxidant activity of various solvent extracts of Piper mullesua fruit were analyzed. The powdered fruit samples were subjected for the extraction by using different solvents such as petroleum ether, chloroform, ethyl acetate, methanol and hot water. The extracts were subjected to quantification of total phenolics, tannins and flavonoids. The in vitro antioxidant assay such as DPPH, ABTS, FRAP, superoxide radical scavenging assay and phosphomolybdenum assays were carried out. Among the different extracts of P. mullesua fruit studied, the ethyl acetate extract depicted the maximum amount of phenolics (697.32 mg GAE/g extract), tannin (683.04 mg GAE/g extract) and flavonoids (398.94 mg RE/g extract). Highest percentage of DPPH (IC50: 31.51 µg/mL), ABTS (64111.11 µM TE/g extract), FRAP (263.76 mM Fe (II)/mg extract), Superoxide (52.79%) and phosphomolybdenum radical scavenging activity (79.83 mg AAE/g extract) were recorded in the ethyl acetate extract of P. mullesua fruit. The present study provides evidence that ethyl acetate extract of Piper mullesua fruit possesses excellent in vitro antioxidant activity, which can be a potential source of natural antioxidants, should be further exploited for its use in clinical medicine. [ABSTRACT FROM AUTHOR]

Background: Trauma management is an ever evolving part of orthopaedics and with the latest advancement in metallurgy, crafting processes and biomechanical studies, the implants used have been continuously changing. Distal femur fractures occur at a major joint and their management has always been complicated ranging from preoperative, intra-op to postoperative period. All the studies and research are aimed towards achieving the best functional outcome and functional outcome studies help in collecting and assimilation of the data for the same which further help in modification of techniques to further improve the same. This study focusses on the outcomes achieved by using the distal femur locking compression plate for the management of fractures distal end of femur. Materials and Methods: Prospective, unicentric, observational study from June 2018 - February 2020. All patients with distal femur facture were included. Pathological fractures, open fractures, fractures older than 3 weeks and patients with ipsilateral proximal tibia, patella and proximal femur were excluded. Surgical approach was surgeon's choice. Functional outcome was measured using Knee Society Knee Scoring system at 6weeks, 3 months, 6months and 12 months. Functional outcome measured at 12 months was used for evaluation. Results: 28 patients, 19(67.9%) males and 9(32.1%) females. 89.47% of males sustained the fracture due to road traffic accident and 66.66% of females sustained due to fall and all belonged to age group more than 60 years. The average range of motion achieved was 112.3 ± 8.2. Functional outcome at 12 months 71.4% patients had excellent, 25% had good and 3.6% patients had fair and no patient had poor outcome. Conclusion: Distal femur fracture is more common in the age group of 21-40 years. The average range of motion of knee achieved in Type A and Type B fractures are significantly better than Type C fractures. Excellent functional outcome was statistically significant in Type A and Type B fractures when compared to Type C fractures. [ABSTRACT FROM AUTHOR]

In this paper, a dielectric barrier discharge plasma based atmospheric pressure plasma jet has been generated in a floating helix and floating end ring electrode configuration using mixture of argon and nitrogen gases (50:50 ratio). This configuration is subjected to a range of supply frequencies (10–25 kHz) and supply voltages (6.5–9.5 kV) at a fixed rate of gas flow rate (i.e., 1 l/min). The electrical characterization of the plasma jet has been carried out using a high voltage probe and current transformer. The current–voltage characteristics have been analyzed, and the power consumed by the device has been estimated at different applied combinations of supply frequency and voltages for optimum power consumption and maximum jet length. A comparative analysis of the results of the above experiments has shown that maximum power consumed by the device in helix electrode configuration with end ring is 19 W for (Ar+N2) mixture as compared to only 12 mW and 7.7 mW for Ar and He gas respectively (With end ring), this may be due to the main ionization mechanisms which are different depending on the working gas. Furthermore, maximum jet length of 42 mm has been obtained for He gas at 6 kV/25 kHz due to penning ionization process in comparison to jet lengths of only 32 mm for Ar gas and jet length of only 26 mm for Ar+N2 mixture. The obtained average power consumed and maximum jet length for mixture of (Ar+N2) gases are 6.5 W and 26 mm. [ABSTRACT FROM AUTHOR]

In this study, both model analysis and electrical characterization of a dielectric barrier discharge based argon plasma jet have been carried at atmospheric pressure in a pin electrode configuration. The plasma and fluid dynamics modules of COMSOL multi-physics code have been used for the modeling of the plasma jet. The plasma parameters, such as, electron density, electron temperature and electrical potential have been analyzed with respect to the electrical parameters, i.e., supply voltage and supply frequency with and without the flow of gas. In all the experiments, gas flow rate has been kept constant at 1 liter per minute. This electrode configuration is subjected to a range of supply frequencies (10-25 kHz) and supply voltages (3.5-6.5 kV). The power consumed by the device has been estimated at different applied combinations (supply voltage & frequency) for optimum power consumption at maximum jet length. The maximum power consumed by the device in this configuration for maximum jet length of ∼26 mm is just ∼1 W. [ABSTRACT FROM AUTHOR]

Data migration is the process of transferring data between storage types, formats, or computer systems. Data migration can be done for Server or storage, equipment Website consolidation, replacements or upgrades. It is required when organizations or individuals update computer systems, or when systems merge. Database Migration Tool Using SAAS model is a tool and Java API to migrate data from different sources (XML, DBF and JDBC) to another (DBF, JDBC, TXT). Data migration is preceded by the obsolete method of the programmer performing all the operations that included Copying the data, saving the files and Restoring the data in the database manually. Hence Data Migration in an automated programmatic approach. The conversion configuration is specified by an XML file defining source, target and Schema transformations of the data and also data on the old system is mapped to the new system by providing a design for data extraction and data loading. [ABSTRACT FROM AUTHOR]

In this paper, a dielectric barrier discharge plasma based atmospheric pressure plasma jet has been generated in a floating helix and floating end ring electrode configuration using argon and helium gases. This configuration is subjected to a range of supply frequencies (10-25 kHz) and supply voltages (2-6 kV) at a fixed rate of gas flow rate (i.e., 1 l/min). The electrical characterization of the plasma jet has been carried out using a high voltage probe and current transformer. The current-voltage characteristics have been analyzed, and the consumed power has been estimated at different applied combinations for optimum power consumption at maximum jet length. The obtained optimum power and jet length for argon and helium gases are 12 mW and 32 mm, and 7.7 mW and 42 mm, respectively. It is inferred that besides the electrode configurations, the discharge gas is also playing a significant role in the low power operation of the cold plasma jet at maximum jet length. The obtained results are interpreted on the basis of penning processes. [ABSTRACT FROM AUTHOR]

Purpose: The ideal treatment for patients presenting with bilateral anterior cruciate ligament (ACL) deficiency remains controversial. The purpose was to evaluate cost and functional results after one-stage bilateral ACL reconstruction using hamstring tendon autograft. Methods: This prospective study evaluated the mid-termfunctional outcome of 7 patients (14 knees) who had undergone one-stage bilateral ACL reconstruction. Results: The median length of hospital stay was 4 (3-5) days. The duration of rehabilitation process in patients with bilateral ACL reconstruction was 6-8weeks. The mean Lysholm scorewas 93 (90-95), IKDC score 78.7 (74.7-82.75), and Tegner activity score level 5. The median time to return to full-timework and to full sports was 9 weeks and 8 months respectively. 6 patients (85.7%) in the study group returned to their pre-injury level of activity. Conclusions: Mid-term clinical results suggested that one-stage bilateral ACL reconstruction using hamstring tendon autograft is clinically effective. For patients presenting with bilateral ACL-deficient knees, one-stage bilateral ACL reconstruction is reproducible, cost effective and does not compromise functional results. [ABSTRACT FROM AUTHOR]

Camellia sinensis is the ethno medicinal plant widely used traditionally for the treatment of many inflammatory diseases because of plenty of source of flavonoid, saponin and terpenes. The present study has been focused to assess the anti-inflammatory activity of Camellia sinensis by using cotton pellet granuloma in rat for the better therapeutic activity. The aquous extract of above plant was administered orally at a dose of 400 mg/kg and 800 mg/kg body weight for 7 days after implantation of sterilized cotton pellet in axilla and groin region of the rat. A significant (P ⩽ 0.01) reduction of wet weight and dry weight of cotton pellet at 31.77%, 45.10% and 19.51%, 48.15% was observed. A significant reduction (P⩽0.01) in total leukocyte number, ESR and spleen weight was found at the same dose. A better activity was observed at 800mg/kg body weight. [ABSTRACT FROM AUTHOR]

Aims: 1) To design a cost effective, easy to handle, non invasive, safe and accurate instrument that measures the impedance changes during dialysis using the principle of segmental Bioelectrical impedance, 2) To test the instrument on haemodialysis patients for a) Repeatability in impedance value changes which correspond to fluid losses during dialysis and b) To correlate the weight change with impedance change during the dialysis session. Materials and Methods: The bioimpedance device was developed in house in the Department of Physiology at our centre, the main components of which included a battery source, an oscillator to convert incoming DC to AC, sensing and detecting electrodes (ECG electrodes, amplifier, and display unit. The total cost of construction of the machine was Rs 10000(ten thousand only). The instrument was tested for segmental impedance in the upper limb on 30 healthy volunteers for standardization. In the second part of the study, Impedance values were measured in 20 patients with varying weight gain through the four hours of dialysis. Repeatability of the values was tested on these patients during subsequent dialysis sessions. The delta change in weight was correlated with the delta change in bioimpedance in each patient. Results: Segmental bioimpedance was found to be specific to each patient tested. For healthy volunteers, segmental bioimpedance ranged from 0.525 KO (kilo ohms) to 1.710 KO. The study was conducted on 12 males and 8 females with a mean age of 38±12.6 yrs. The mean interdialytic weight gain was 2.8±1.6 kg. Change in segmental bioimpedance during dialysis varied from 0.180 KO to 0.565 KO depending on the amount of fluid removal. The maximal change in bioimpedance was noted in the first and second hour of dialysis. We also correlated the delta change in impedance with delta change in weight for each patient in subsequent dialysis sessions. For a given patient, the weight loss could be correlated with the change in bioimpedance with an accuracy of ~90%. Conclusions: In this pilot study, we have been able to indigenously device a portable, cost effective, easy to handle and accurate segmental bioimpedance measuring instrument for use in haemodialysis patients to measure weight loss online during dialysis. [ABSTRACT FROM AUTHOR]